An interdisciplinary research group at Wake Forest University aims to develop theory, algorithms, computational tools, and research methodologies for network modeling of redox-regulated events in human cells. Recent research indicates that redox-regulated networks are central to the communication of cellular signals under a variety of normal and disease conditions, including cancer, neurodegenerative diseases, and aging. This project will 1) identify a comprehensive set of cellular proteins modified at cysteine residues as a result of redox-dependent signaling; 2) correlate the concentration of a given cellular perturbant (i.e., oxidant and anti-oxidant) and its associated redox signal; 3) associate networks with particular perturbants; and 4) produce both topological and dynamic models of the cellular network associated with these pathways. These models will be overlaid on existing data on protein/protein interactions and kinase cascades to produce a more comprehensive model of cellular regulation and its biological outcomes. A unique modeling strategy will use computational algebra and Bayesian network analysis to model these events. The computer algebra techniques construct next-state functions as polynomials over a finite field. Consensus models that represent the underlying biological network will identify interdependencies of the protein modifications and biological responses. Bayesian network analysis produces probabilistic dependencies among the variables. The combination of Bayesian and computational algebra approaches will positively impact the network reliability and ability to predict the biological outcomes of oxidant and anti-oxidant perturbations. Such models can only be produced with large and consistent data sets, and the new reagents and procedures developed by the co-investigators greatly extend the currently limited methods to identify the components of redox-dependent signaling pathways on a large-state, "proteomic" basis. This project will assess oxidative modifications of proteins and associated biological endpoints for a set of cellular perturbants, providing previously unattainable biological data on redox-dependent signaling. With these reagents and methods and the combination of mathematical tools, this research group is in a unique position to undertake a systems biology approach and robustly model redox signal transduction pathways for the first time. The project's outcomes - a comprehensive list of the components of redox signaling pathways, their biological consequences, and topological and dynamic network models - will provide a systems biology understanding of redox signaling networks in human cells.